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Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

Abstract

Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks.

Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques.

Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa.

Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.

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Hybridization of Stochastic Local Search and Genetic Algorithm for Human Resource Planning Management

References Babaei, M., Zahra, G., & Soudabeh, A. (2015). Challenges of Enterprise Resource Planning implementation in Iran large organizations. Information Systems, 54, 15-27, http://dx.doi.org/10.1016/j.is.2015.05.003 Borštnar Kljajić, M., Kljajić, M., Škraba, A., Kofjač, D., & Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model. System Dynamics Review, 27(3), 270-293, http://dx.doi.org/10.1002/sdr.460 Cabanillas, C., Resinas, M., del-Río-Ortega, A

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Towards Understanding Collaborative Learning in the Social Media Environment

.). Hershey, PA: Information Science Reference; 1559-1572. Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D., & Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model, System Dynamics Review , 27(3): 270-293. DOI: 10.1002/sdr.460 Kreijns, K., Kirschner, P. A. & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Computers in Human Behavior , 19(3), 335-353. DOI

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Comparative analysis of collaborative and simulation based learning in the management environment

performance. In Strategic Information Systems: Concepts, Methodologies, Tools, and Applications , Hunter MG (ed.). Hershey, PA: Information Science Reference; 1559-1572. Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D. & Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model, System Dynamics Review , 27(3), 270-293, http://dx.doi.org/10.1002/sdr.460 Kljajić Borštnar, M. (2012). Towards understanding collaborative learning in the social media environment. Organizacija , 45

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Impacts of the Implementation of a Project Management Information System – a Case Study of a Small R&D Company

-7863(98)00026-X Kaiser, M.G., Ahlemann, F. (2010). Measuring Project Management Information Systems Success: Towards a Conceptual Model and Survey Instrument. ECIS 2010. Kerzner, H. (2003). Project management, A systems approach to planning, scheduling and controlling . New York: John Wiley and Sons. Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D., Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model. System Dynamics Review , 27(3), 270-293, http://dx.doi.org/10.1002/sdr.460

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Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

. 369–381. Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D., & Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model. System Dynamics Review , 27 (3), 270–293, https://doi.org/10.1002/sdr.460 Kuchinke, K. P. (2000). The role of feedback in management training settings. Human Resource Development Quarterly , 11 (4), 381–401, http://dx.doi.org/10.1002/1532-1096(200024)11:4%3C381::AID-HRDQ5%3E3.0.CO;2-3 Lawrence, M., Goodwin, P., O’Connor, M. & Önkal, D. (2006). Judgmental forecasting

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Smart Simulation for Decision Support at Headquarters

References [1] Simon H. A, Administrative behavior , Cambridge UnivPress, 1965. [2] Mora M., Forgionne G., Gupta J., Cervantes F., & Gelman O., A framework to assess intelligent decision-making support systems . In: Knowledge-Based Intelligent Information and Engineering Systems, p. 63, 2003. [3] Mora M., Forgionne G., Gupta J., Cervantes F., & Gelman O., A framework to assess intelligent decision-making support systems . In: Knowledge-Based Intelligent Information and Engineering Systems, pp. 59-60, 2003. [4] Endsley M. R., “ Toward a

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